As our focus is on the data augmentation techniques, we adopt an existing blind source separation framework called Open-Unmix [1]} as the backbone architecture in our work. Open-Unmix is a hybrid convolutional-recurrent architecture (see Figure REF ) that is open source.
It takes a fixed-length chunk of the Short-time Fourier transform (STFT) spectrogram of the mixture as the input and aims to get the corresponding separated spectrogram of one of the sources at the output. Although the model is trained on fixed-length chunks, at testing time it can be applied to spectrograms of arbitrary length. The parameters of the network is learned by minimizing the difference between the spectrograms of the ground truth and the separation result, calculated in terms of mean square error.
